Walking speed and trunk sway: Influence of an approaching person's gait pattern on collision avoidance
Bibliographic record
Abstract
Gait patterns provide a rich source of person-specific information such as age, sex, identity, and vulnerability. However, it is unknown to what extent person-specific gait information can affect collision avoidance behaviours with an approaching “person”. We sought to determine whether young adults’ spatiotemporal avoidance behaviours were affected by changes to a virtual agent’s gait parameters (i.e., speed or trunk sway). In a virtual environment (FOVE head-mounted display; 70Hz), young adults (n=21) walked along an 18m pathway towards a goal while avoiding an approaching virtual agent. The agent’s walking speed and trunk sway magnitude were a multiples of each participant’s average speed or sway: fast (x1.5m/s), normal (matched), or slow (x0.8m/s); large (2x), normal (matched), or small (0x) respectively. The agent was non-reactive and walked straight forward at a constant speed. Participants’ kinematics were recorded (Qualisys; 120Hz) to examine avoidance behaviours of initiation of path deviation and medial-lateral clearance at the time of crossing. Statistical analysis revealed that participants initiated a path deviation (i.e., estimate of time-to-contact, TTC) significantly earlier (F(2,40)=11.31, p<.001, f=.69) when the agent was walking fast (M=3.93s, SD=.56) as opposed to normal (M=4.31s, SD=.33) and slow (M=4.41s, SD=.44) walking speeds. However, the agent’s trunk sway magnitudes did not affect participants’ initiation of path deviation or medial-lateral clearance at crossing. Participants appear not to use temporal information to initiate an avoidance, but rather a point in space (i.e., TTC was affected by approach speed) due to awareness that the agent was non-reactive and always approaching. The agent’s sway magnitude did not affect medial-lateral avoidance behaviours most likely because there was little observable difference between conditions (i.e., ~3° normal sway). Conceivably, the study’s environmental conditions may underrepresent people’s behaviours in real-world. Future work is needed to understand the perception of an approaching person’s gait characteristics on collision avoidance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".